基于最优邻域特征加权的点云引导滤波算法  

Point Cloud Guided Filtering Algorithm Based on Optimal Neighborhood Feature Weighting

作　　者：徐志博 吕秋娟 甘鑫斌 谭佳敏 刘永生[1,2] Xu Zhibo;LüQiujuan;Gan Xinbin;Tan Jiamin;Liu Yongsheng(Key Laboratory of Road Construction Technology and Equipment of Ministry of Education,School of Construction Machinery,Chang’an University,Xi’an 710064,Shaanxi,China;AVIC JONHON Optronic Technology Co.,LTD.,Luoyang 471003,Henan,China;Department of Basics,Rocket Force University of Engineering,Chinese People’s Liberation Army,Xi’an 710025,Shaanxi,China)

机构地区：[1]长安大学机械工程学院道路施工技术与装备教育部重点实验室,陕西西安710064 [2]中航光电科技股份有限公司,河南洛阳471003 [3]中国人民解放军火箭军工程大学基础部,陕西西安710025

出　　处：《激光与光电子学进展》2024年第14期219-226,共8页Laser & Optoelectronics Progress

基　　金：陕西省自然科学基金(2022JM-295)。

摘　　要：在点云去噪过程中,当点云数据中的大尺度噪声点被去除后,点云周围通常还混杂着难以直接去除的小尺度噪声点,严重影响重建表面的光滑性,导致模型出现一定程度的特征失真。针对小尺度噪声点,提出了一种基于最优邻域特征加权的点云引导滤波算法。首先基于信息熵函数选取最优初始邻域,结合曲面变化度、法线变化度和距离特征实现特征点识别,然后再对特征点的邻域进行自适应生长以获得平滑邻域,最后利用曲面变化度加权调整引导滤波算法,实现对复杂曲面零件特征和非特征部分的各向异性光顺。实验结果表明,所提算法相较于几种常用的光顺算法对噪声点云的平滑效果更明显,在特征保持方面表现更好,并且在效率方面更优。In the process of point cloud denoising,after removing largescale noise points from the point cloud data,there are usually small noise points mixed around the point cloud that are difficult to directly remove.This seriously affects the smoothness of the reconstructed surface and leads to a certain degree of feature distortion in the model.Thus,for smallscale noise points,this study proposes a pointcloudguided filtering algorithm based on optimal neighborhood feature weighting.The optimal initial neighborhood is selected based on the information entropy function,and feature points are identified by combining surface and normal variations with distance features.The neighborhoods of the feature points are adaptively grown to obtain a smooth neighborhood.The guided filtering algorithm is adjusted by surface variation weighting to achieve anisotropic smoothness of the feature and nonfeature parts of the complex surface part.As evidenced by experimental results,the proposed algorithm exhibits a more obvious smoothing effect on noisy point clouds,performs better in feature retention,and is significantly more efficient than several commonly used smoothing algorithms.

关 键 词：点云去噪 引导滤波 最优邻域 邻域重构 特征点识别 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]